3.1 HeLa

This tab shows historical overview of your instrument performance.

3.2 Predictions

This tab is used to predict quality of your experimental sample

Screenshot of Prediction tab using example data

3.3 Database

This tab is used to generate CSV (database) file for your application.

4.1 Observe and log your samples

4.2 Prediction with your data

4.3 Creating your own database

You will need to have database (CSV file) named allFeatures.csv that contains summary of the features, instrument details, filenames and dates. An example of the file headers and data is shown below.

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NOTE: Make sure that the order of your headers are identical to the screenshot provided.

This can be generated using the [R script provided OR “Database” tab of the application at http://shiny.maths.usyd.edu.au/QCMAP/]. In order to create this file from multiple MaxQuant output files, zip the output files by studies and upload multiple zip files to combine them all together to one CSV file.

For example, given a file structure as follows,

./
├── study1
│   ├── Oxidation\ (M)Sites.txt
│   ├── aifMsms.txt
│   ├── allPeptides.txt
│   ├── evidence.txt
│   ├── libraryMatch.txt
│   ├── matchedFeatures.txt
│   ├── modificationSpecificPeptides.txt
│   ├── ms3Scans.txt
│   ├── msScans.txt
│   ├── msms.txt
│   ├── msmsScans.txt
│   ├── mzRange.txt
│   ├── parameters.txt
│   ├── peptides.txt
│   ├── proteinGroups.txt
│   ├── summary.txt
│   └── tables.pdf
└── study2
    ├── Oxidation\ (M)Sites.txt
    ├── aifMsms.txt
    ├── allPeptides.txt
    ├── evidence.txt
    ├── libraryMatch.txt
    ├── matchedFeatures.txt
    ├── modificationSpecificPeptides.txt
    ├── ms3Scans.txt
    ├── msScans.txt
    ├── msms.txt
    ├── msmsScans.txt
    ├── mzRange.txt
    ├── parameters.txt
    ├── peptides.txt
    ├── proteinGroups.txt
    ├── summary.txt
    └── tables.pdf

Compress study1 and study2 as separate zip files as input.